System and method for vehicle wheel detection

ABSTRACT

A system and method for vehicle wheel detection is disclosed. A particular embodiment can be configured to: receive training image data from a training image data collection system; obtain ground truth data corresponding to the training image data; perform a training phase to train one or more classifiers for processing images of the training image data to detect vehicle wheel objects in the images of the training image data; receive operational image data from an image data collection system associated with an autonomous vehicle; and perform an operational phase including applying the trained one or more classifiers to extract vehicle wheel objects from the operational image data and produce vehicle wheel object data.

PRIORITY PATENT APPLICATIONS

This patent application draws priority from U.S. non-provisional patentapplication Ser. No. 15/456,219; filed Mar. 10, 2017. This patentapplication also draws priority from U.S. non-provisional patentapplication Ser. No. 15/456,294; filed Mar. 10, 2017. This patentapplication also draws priority from U.S. non-provisional patentapplication Ser. No. 15/917,331; filed Mar. 9, 2018. This presentnon-provisional patent application draws priority from the referencedpatent applications. The entire disclosure of the referenced patentapplications is considered part of the disclosure of the presentapplication and is hereby incorporated by reference herein in itsentirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2020, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for image processing,vehicle control systems, and autonomous driving systems, and moreparticularly, but not by way of limitation, to a system and method forvehicle wheel detection.

BACKGROUND

In autonomous driving systems, the successful perception and predictionof the surrounding driving environment and traffic participants arecrucial for making correct and safe decisions for control of theautonomous or host vehicle. In the current literature and application ofvisual perception, techniques such as object recognition, twodimensional (2D) object detection, and 2D scene understanding (orsemantic segmentation) have been widely studied and used. With theassistance of fast-developing deep learning techniques and computationalpower (such as graphics processing units [GPUs]), these visualperception techniques have been successfully applied for use withautonomous or host vehicles. Compared with these 2D perception methods,full three dimensional (3D) perception techniques, however, are lessstudied because of the difficulty in getting robust ground truth dataand the difficulty in properly training the 3D models. For example,correct annotation of the 3D bounding box for 3D object detectionrequires accurate measurement of the extrinsic and intrinsic cameraparameters as well as the motion of the autonomous or host vehicle,which are usually difficult or impossible to obtain. Even if groundtruth data can be obtained, the 3D model is difficult to train becauseof the limited amount of training data and inaccurate measurements. As aresult, less-expensive and much less functionally-capable alternativesolutions have been used in these visual perception applications.

SUMMARY

Vehicle wheels are an important feature for determining the exactlocation and pose of moving vehicles. Vehicle pose can include thevehicle heading, orientation, speed, acceleration, and the like.However, the use of vehicle wheel features for vehicle control is oftenneglected in current computer vision and autonomous driving literatureand applications. In the various example embodiments disclosed herein, asystem and method for vehicle wheel detection using image segmentationis provided. In an example embodiment, the system comprises threecomponents: 1) data collection and annotation, 2) model training usingdeep convolutional neural networks, and 3) real-time model inference. Toleverage the state-of-the-art deep learning models and trainingstrategies, the various example embodiments disclosed herein form thewheel detection problem as a two-class segmentation task, and train on adeep neural network that excels on multi-class semantic segmentationproblems. Test results demonstrate that the systems disclosed herein cansuccessfully detect vehicle wheel features under complex drivingscenarios in real-time. The various example embodiments disclosed hereincan be used in applications, such as 3D vehicle pose estimation andvehicle-lane distance estimation, among others.

Vehicle wheels can be used for vehicle feature analysis for at leastthree reasons as follows: 1) the perception and prediction of othertraffic participants are mostly about their trajectory on the roadsurface, where wheels can provide the best measurement as they are thevehicle component most adjacent to the road surface; 2) wheels canprovide a robust estimate of the vehicle pose, as vehicles generallyhave four or more wheels to serve as reference points; and 3) wheels areconceptually easy to detect because of their uniform shape and locationwithin the vehicle. When we obtain accurate wheel feature segmentationanalysis for a given vehicle, we can obtain or infer valuable vehicleinformation, such as pose, location, intention, and trajectory. Thisvehicle information can provide significant benefits for the perception,localization, and planning systems for autonomous driving.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which anin-vehicle image processing module of an example embodiment can beimplemented;

FIG. 2 illustrates an image fetched from a camera (upper image half) andits corresponding wheel annotation result (lower image half);

FIG. 3 illustrates the offline training phase (a first phase) used toconfigure or train the autonomous vehicle wheel detection system, andthe classifiers therein, in an example embodiment;

FIG. 4 (lower image half) illustrates an example ground truth label mapthat can be used for training a segmentation model according to anexample embodiment; FIG. 4 (upper image half) also illustrates theblended visualization of the original example image combined with theground truth;

FIG. 5 illustrates a second phase for operational or simulation use ofthe autonomous vehicle wheel detection system in an example embodiment;

FIG. 6 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 4, among other training images; FIG. 6 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result;

FIG. 7 (lower image half) illustrates another example ground truth labelmap that can be used for training the segmentation model according to anexample embodiment; FIG. 7 (upper image half) also illustrates theblended visualization of the original example image combined with theground truth;

FIG. 8 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 7, among other training images; FIG. 8 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result;

FIG. 9 (lower image half) illustrates yet another example ground truthlabel map that can be used for training the segmentation model accordingto an example embodiment; FIG. 9 (upper image half) also illustrates theblended visualization of the original example image combined with theground truth;

FIG. 10 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 9, among other training images; FIG. 10 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result;

FIG. 11 is a process flow diagram illustrating an example embodiment ofa system and method for vehicle wheel detection; and

FIG. 12 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forvehicle wheel detection are described herein. An example embodimentdisclosed herein can be used in the context of an in-vehicle controlsystem 150 in a vehicle ecosystem 101. In one example embodiment, anin-vehicle control system 150 with an image processing module 200resident in a vehicle 105 can be configured like the architecture andecosystem 101 illustrated in FIG. 1. However, it will be apparent tothose of ordinary skill in the art that the image processing module 200described and claimed herein can be implemented, configured, and used ina variety of other applications and systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and an imageprocessing module 200 of an example embodiment can be implemented. Thesecomponents are described in more detail below. Ecosystem 101 includes avariety of systems and components that can generate and/or deliver oneor more sources of information/data and related services to thein-vehicle control system 150 and the image processing module 200, whichcan be installed in the vehicle 105. For example, a camera installed inthe vehicle 105, as one of the devices of vehicle subsystems 140, cangenerate image and timing data that can be received by the in-vehiclecontrol system 150. The in-vehicle control system 150 and the imageprocessing module 200 executing therein can receive this image andtiming data input. As described in more detail below, the imageprocessing module 200 can process the image input and extract objectfeatures, which can be used by an autonomous vehicle control subsystem,as another one of the subsystems of vehicle subsystems 140. Theautonomous vehicle control subsystem, for example, can use the real-timeextracted object features to safely and efficiently navigate and controlthe vehicle 105 through a real world driving environment while avoidingobstacles and safely controlling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute the imageprocessing module 200 for processing image data received from one ormore of the vehicle subsystems 140. The data processor 171 can becombined with a data storage device 172 as part of a computing system170 in the in-vehicle control system 150. The data storage device 172can be used to store data, processing parameters, and data processinginstructions. A processing module interface 165 can be provided tofacilitate data communications between the data processor 171 and theimage processing module 200. In various example embodiments, a pluralityof processing modules, configured similarly to image processing module200, can be provided for execution by data processor 171. As shown bythe dashed lines in FIG. 1, the image processing module 200 can beintegrated into the in-vehicle control system 150, optionally downloadedto the in-vehicle control system 150, or deployed separately from thein-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as web sites, servers, central controlsites, or the like. The network resources 122 can generate and/ordistribute data, which can be received in vehicle 105 via in-vehicleweb-enabled devices 130 or user mobile devices 132. The networkresources 122 can also host network cloud services, which can supportthe functionality used to compute or assist in processing image input orimage input analysis. Antennas can serve to connect the in-vehiclecontrol system 150 and the image processing module 200 with the datanetwork 120 via cellular, satellite, radio, or other conventional signalreception mechanisms. Such cellular data networks are currentlyavailable (e.g., Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-baseddata or content networks are also currently available (e.g., SiriusXM™,HughesNet™, etc.). The conventional broadcast networks, such as AM/FMradio networks, pager networks, UHF networks, gaming networks, WiFinetworks, peer-to-peer networks, Voice over IP (VoIP) networks, and thelike are also well-known. Thus, as described in more detail below, thein-vehicle control system 150 and the image processing module 200 canreceive web-based data or content via an in-vehicle web-enabled deviceinterface 131, which can be used to connect with the in-vehicleweb-enabled device receiver 130 and network 120. In this manner, thein-vehicle control system 150 and the image processing module 200 cansupport a variety of network-connectable in-vehicle devices and systemsfrom within a vehicle 105.

As shown in FIG. 1, the in-vehicle control system 150 and the imageprocessing module 200 can also receive data, image processing controlparameters, and training content from user mobile devices 132, which canbe located inside or proximately to the vehicle 105. The user mobiledevices 132 can represent standard mobile devices, such as cellularphones, smartphones, personal digital assistants (PDA's), MP3 players,tablet computing devices (e.g., iPad™), laptop computers, CD players,and other mobile devices, which can produce, receive, and/or deliverdata, image processing control parameters, and content for thein-vehicle control system 150 and the image processing module 200. Asshown in FIG. 1, the mobile devices 132 can also be in datacommunication with the network cloud 120. The mobile devices 132 cansource data and content from internal memory components of the mobiledevices 132 themselves or from network resources 122 via network 120.Additionally, mobile devices 132 can themselves include a GPS datareceiver, accelerometers, WiFi triangulation, or other geo-locationsensors or components in the mobile device, which can be used todetermine the real-time geo-location of the user (via the mobile device)at any moment in time. In any case, the in-vehicle control system 150and the image processing module 200 can receive data from the mobiledevices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the image processing module 200. The vehicle105 may include more or fewer subsystems and each subsystem couldinclude multiple elements. Further, each of the subsystems and elementsof vehicle 105 could be interconnected. Thus, one or more of thedescribed functions of the vehicle 105 may be divided up into additionalfunctional or physical components or combined into fewer functional orphysical components. In some further examples, additional functional andphysical components may be added to the examples illustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an O2 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the image processing module 200, the GPS transceiver, and one ormore predetermined maps so as to determine the driving path for thevehicle 105. The autonomous control unit may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 105. In general,the autonomous control unit may be configured to control the vehicle 105for operation without a driver or to provide driver assistance incontrolling the vehicle 105. In some embodiments, the autonomous controlunit may be configured to incorporate data from the image processingmodule 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, andother vehicle subsystems to determine the driving path or trajectory forthe vehicle 105. The vehicle control system 146 may additionally oralternatively include components other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 142, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 142, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 and theimage processing module 200, move in a controlled manner, or follow apath or trajectory based on output generated by the image processingmodule 200. In an example embodiment, the computing system 170 can beoperable to provide control over many aspects of the vehicle 105 and itssubsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, and imageprocessing module 200, as being integrated into the vehicle 105, one ormore of these components could be mounted or associated separately fromthe vehicle 105. For example, data storage device 172 could, in part orin full, exist separate from the vehicle 105. Thus, the vehicle 105could be provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 105could be communicatively coupled together in a wired or wirelessfashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the image processing module200 based on a variety of factors including, the context in which theuser is operating the vehicle (e.g., the location of the vehicle, thespecified destination, direction of travel, speed, the time of day, thestatus of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein.

In a particular embodiment, the in-vehicle control system 150 and theimage processing module 200 can be implemented as in-vehicle componentsof vehicle 105. In various example embodiments, the in-vehicle controlsystem 150 and the image processing module 200 in data communicationtherewith can be implemented as integrated components or as separatecomponents. In an example embodiment, the software components of thein-vehicle control system 150 and/or the image processing module 200 canbe dynamically upgraded, modified, and/or augmented by use of the dataconnection with the mobile devices 132 and/or the network resources 122via network 120. The in-vehicle control system 150 can periodicallyquery a mobile device 132 or a network resource 122 for updates orupdates can be pushed to the in-vehicle control system 150.

System and Method for Vehicle Wheel Detection

In the various example embodiments disclosed herein, a system and methodfor vehicle wheel detection using image segmentation is provided. In anexample embodiment, the system comprises three components: 1) datacollection and annotation, 2) model training using deep convolutionalneural networks, and 3) real-time model inference. To leverage thestate-of-the-art deep learning models and training strategies, thevarious example embodiments disclosed herein form the wheel detectionproblem as a two-class segmentation task, and train on a deep neuralnetwork that excels on multi-class semantic segmentation problems. Whenthe system obtains accurate wheel feature segmentation analysis for agiven vehicle, the system can obtain or infer valuable vehicleinformation, such as pose, location, intention, and trajectory. Thisvehicle information can provide significant benefits for the perception,localization, and planning systems for autonomous driving. In variousexample embodiments described herein, the components of the vehiclewheel detection system are described below.

Data Collection and Annotation

In various example embodiments, the wheel segmentation problem can bedefined in different ways, such as, 1) a bounding-box regression problemthat requires only the location of the four corners of a rectangularbounding box, 2) a semantic segmentation problem which requirespixel-level labeling for the wheel area, or 3) an instance segmentationproblem, which requires an assignment of different instance identifiernumbers (IDs) for every single wheel. The example embodiments describedherein provide an annotation paradigm that is efficient and suitable forall possible tasks. As vehicle wheels generally share similar visibleshapes, such as a circle or an ellipse, the processing performed by theexample embodiments transforms the vehicle wheel annotation task into acontour annotation task. That is, the example embodiments can beconfigured to identify and render the outline or contour surroundingevery vehicle wheel detected in an input image. From the vehicle wheelcontours, the example embodiments can be configured to generatecorresponding detection bounding boxes by extracting the extreme valuesfor all four directions (top, bottom, left, and right) of each vehiclewheel contour and generating the corresponding bounding boxes from theseextreme values. Additionally, the example embodiments can be configuredto obtain the semantic segmentation labels corresponding to the vehiclewheel contours by filling in the interior regions defined by the wheelcontours. Finally, the example embodiments can also obtain the vehiclewheel instance labels by counting the number of closed vehicle wheelcontours and generating different instance identifier numbers (IDs) foreach instance of a vehicle wheel detected in the input image. As aresult, the example embodiments can generate a variety of informationbased on the vehicle wheel contours identified in an input image.Importantly, drawing vehicle wheel contours is very easy for the humanlabelers, thus helping us to build a large machine learning trainingdataset efficiently. Thus, machine learning techniques can be used toenable the example embodiments to gather raw training image data and totrain a machine learning model to identify and annotate vehicle wheelcontours in an input image. Then, the example embodiments can generatethe variety of information described above based on the identifiedvehicle wheel contours. A sample raw input image and the vehicle wheelcontour labeling result produced by an example embodiment are shown inFIG. 2.

FIG. 2 illustrates a raw input image (FIG. 2, upper image half) fetchedfrom a camera of an autonomous vehicle and the corresponding vehiclewheel contour labeling or annotation result (FIG. 2, lower image half inreverse color) produced by an example embodiment. The dashed arrowsshown in FIG. 2 were added to highlight the association between eachinstance of a vehicle wheel contour annotation and the portion of theraw input image from which the vehicle wheel contour annotation wasderived. As described in more detail below, a trained machine leaningmodel can be used to generate the vehicle wheel contour annotations fromthe raw input images. This contour-level vehicle wheel annotationenabled by the example embodiments disclosed herein provides severalimportant benefits, including allowing a transformation of the detectedvehicle wheel object information to any desired format.

Model Training

In the example embodiments described herein, supervised learning methodscan be used for classification of objects, object features, and objectrelationships captured in a set of input images. Supervised learningmethods include a process of training classifiers or models using a setof training or test data in an offline training phase. By exactingpredefined features and manually-annotated labels of each object (e.g.,vehicle wheels) in the input images, the example embodiments can trainone or more machine learning classifiers on many static training images.Additionally, the example embodiments can train machine learningclassifiers on training image sequences. After the training phase, thetrained machine learning classifiers can be used in a second phase, anoperational or inference phase, to receive real-time images andeffectively and efficiently detect each vehicle's wheel features in thereceived images. The training and operational use of the machinelearning classifiers in the example embodiment is described in moredetail below.

Referring now to FIG. 3, an example embodiment disclosed herein can beused in the context of an autonomous vehicle wheel detection system 210for autonomous vehicles. The autonomous vehicle wheel detection system210 can be included in or executed by the image processing module 200 asdescribed above. The autonomous vehicle wheel detection system 210 caninclude one or more vehicle wheel object contour classifiers 211, whichcan correspond to the machine learning classifiers described herein. Itwill be apparent to those of ordinary skill in the art in view of thedisclosure herein that other types of classifiers or models can beequivalently used. FIG. 3 illustrates the offline training phase (afirst phase) used to configure or train the autonomous vehicle wheeldetection system 210, and the classifiers 211 therein, in an exampleembodiment based on training image data 201 and manually annotated imagedata 203 representing ground truth. In the example embodiment, atraining image data collection system 201 can be used gather perceptiondata to train or configure processing parameters for the autonomousvehicle wheel detection system 210 with training image data. Asdescribed in more detail below for an example embodiment, after theinitial training phase, the autonomous vehicle wheel detection system210 can be used in an operational, inference, or simulation phase (asecond phase) to generate image feature predictions and wheel contourfeature detections based on image data received by the autonomousvehicle wheel detection system 210 and based on the training theautonomous vehicle wheel detection system 210 receives during theinitial offline training phase.

Referring again to FIG. 3, the training image data collection system 201can include an array of perception information gathering devices orsensors that may include image generating devices (e.g., cameras), lightamplification by stimulated emission of radiation (laser) devices, lightdetection and ranging (LIDAR) devices, global positioning system (GPS)devices, sound navigation and ranging (sonar) devices, radio detectionand ranging (radar) devices, and the like. The perception informationgathered by the information gathering devices at various trafficlocations can include traffic or vehicle image data, roadway data,environmental data, distance data from LIDAR or radar devices, and othersensor information received from the information gathering devices ofthe data collection system 201 positioned adjacent to particularroadways (e.g., monitored locations). Additionally, the data collectionsystem 201 can include information gathering devices installed in movingtest vehicles being navigated through pre-defined routings in anenvironment or location of interest. Some portions of the ground truthdata can also be gathered by the data collection system 201.

To expand the size and to improve the variance of the training imagedataset, the data collection system 201 can collect images from bothwide-angle and long-focus cameras that are installed on vehicles, undera wide range of driving scenarios: local, highway, sunny, cloudy, city,rural, bridge, desert, etc. The training image dataset can be split intoa training dataset that is used for model training, and a testingdataset that is used for model evaluation.

The image data collection system 201 can collect actual images ofvehicles, moving or static objects, roadway features, environmentalfeatures, and corresponding ground truth data under different scenarios.The different scenarios can correspond to different locations, differenttraffic patterns, different environmental conditions, and the like. Theimage data and other perception data and ground truth data collected bythe data collection system 201 reflects truly realistic, real-worldtraffic information related to the locations or routings, the scenarios,and the vehicles or objects being monitored. Using the standardcapabilities of well-known data collection devices, the gathered trafficand vehicle image data and other perception or sensor data can bewirelessly transferred (or otherwise transferred) to a data processor ofa standard computing system, upon which the image data collection system201 can be executed. Alternatively, the gathered traffic and vehicleimage data and other perception or sensor data can be stored in a memorydevice at the monitored location or in the test vehicle and transferredlater to the data processor of the standard computing system.

As shown in FIG. 3, a manual annotation data collection system 203 isprovided to apply labels to features found in the training imagescollected by the data collection system 201. These training images canbe analyzed by human labelers or automated processes to manually definelabels or classifications for each of the features identified in thetraining images. The manually applied data can also include objectrelationship information including a status for each of the objects in aframe of the training image data. For example, manual labelers can drawthe contours of vehicle wheel objects detected in the training imagedatasets. As such, the manually annotated image labels and objectrelationship information can represent the ground truth datacorresponding to the training images from the image data collectionsystem 201. These feature labels or ground truth data can be provided tothe autonomous vehicle wheel detection system 210 as part of the offlinetraining phase as described in more detail below.

The traffic and vehicle image data and other perception or sensor datafor training, the feature label data, and the ground truth data gatheredor calculated by the training image data collection system 201 and theobject or feature labels produced by the manual annotation datacollection system 203 can be used to generate training data, which canbe processed by the autonomous vehicle wheel detection system 210 in theoffline training phase. For example, as well-known, classifiers, models,neural networks, and other machine learning systems can be trained toproduce configured output based on training data provided to theclassifiers, models, neural networks, or other machine learning systemsin a training phase. As described in more detail below, the trainingdata provided by the image data collection system 201 and the manualannotation data collection system 203 can be used to train theautonomous vehicle wheel detection system 210, and the classifiers 211therein, to determine the vehicle wheel contour features correspondingto the objects (e.g., vehicle wheels) identified in the training images.The offline training phase of the autonomous vehicle wheel detectionsystem 210 is described in more detail below.

The example embodiments can train and use machine learning classifiersin the vehicle wheel detection process. These machine learningclassifiers are represented in FIG. 3 as vehicle wheel object contourclassifiers 211. In the example embodiment, the vehicle wheel objectcontour classifiers 211 can be trained with images from the trainingimage dataset. In this manner, the vehicle wheel object contourclassifiers 211 can effectively and efficiently detect the vehicle wheelfeatures of each vehicle from a set of input images. The training of thevehicle wheel object contour classifiers 211 in an example embodiment isdescribed in more detail below.

Referring now to FIG. 4 (upper image half), the diagram illustrates ablended visualization of an original example raw training image combinedwith the ground truth. FIG. 4 illustrates a sample training image thatmay be used by an example embodiment to train the vehicle wheel objectcontour classifiers 211 to process a training image. The raw trainingimage can be one of the training images provided to the autonomousvehicle wheel detection system 210 by the training image data collectionsystem 201 as described above. The training image data from the rawtraining image can be collected and provided to the autonomous vehiclewheel detection system 210, where the features of the raw training imagecan be extracted. Semantic segmentation or similar processes can be usedfor the feature extraction. As well-known, feature extraction canprovide a pixel-level object label and bounding box for each feature orobject identified in the image data. In many cases, the features orobjects identified in the image data will correspond to vehicle wheelobjects. As such, vehicle wheel objects in the input training image canbe extracted and represented with labels and bounding boxes. Thebounding boxes can be represented as a rectangular box of a sizecorresponding to the contour of the extracted vehicle wheel object.Additionally, object-level contour detections for each vehicle wheelobject can also be performed using known techniques. As a result, theautonomous vehicle wheel detection system 210 can obtain or produce, foreach received training image, vehicle wheel object detection datarepresented with labels and bounding boxes and object-level contourdetections for each instance of vehicle wheel objects in the trainingimages. Referring now to FIG. 4 (lower image half), the diagramillustrates an example ground truth label map that can be used fortraining a segmentation model according to an example embodiment.

Because the exact shape and accurate location of a vehicle wheelprovides much more information than a bounding box, an exampleembodiment can adopt a semantic segmentation framework for the vehiclewheel detection task, which is also not over-complicated compared withan instance segmentation task. The formal definition of the problem canbe described as the following:

-   -   Given a raw input RGB (red/green/blue) image I,    -   output a label map R that has the same size as I    -   with vehicle wheel pixels labeled as 1    -   and background pixels labeled as 0.

The example embodiment can process the labeled vehicle wheel contourdata to generate the ground truth by filling in the interior regionsdefined by the vehicle wheel contours and performing dilation to obtainmore positive training samples (e.g., vehicle wheel objects) toalleviate the potential data unbalancing problem.

An example embodiment can use a fully convolutional neural network (FCN)as a machine leaning model trained for the vehicle wheel object contourdetection task as described herein. General forms of FCNs have beenwidely applied to pixel level image-to-image learning tasks. In theexample embodiment, the FCN trained for the vehicle wheel object contourdetection task (e.g., the machine leaning model) can be customized toinclude semantic segmentation using dense upsampling convolution (DUC)and semantic segmentation using hybrid dilated convolution (HDC) asdescribed in the related patent applications referenced above. The FCNfor the vehicle wheel object contour detection task can be pre-trainedon more complex multi-class scene parsing tasks so the learned featurescan speed up the training process. Because the image background containsfar more pixels than the image foreground (e.g., vehicle wheels), theexample embodiment can use a weighted multi-logistic loss function totrain the machine learning model to ensure proper training and alleviateoverfitting. The example embodiment can train the whole machine learningmodel using stochastic gradient descent (SGD) for sufficient iterationsto ensure convergence.

At this point, the offline training process is complete and theparameters associated with the one or more classifiers 211 have beenproperly adjusted to cause the one or more classifiers 211 tosufficiently detect vehicle object wheel features corresponding to theinput image data. After being trained by the offline training process asdescribed above, the one or more classifiers 211 with their properlyadjusted parameters can be deployed in an operational, inference, orsimulation phase (a second phase) as described below in connection withFIG. 5.

Inference

After the FCN training converges, the example embodiments can use thepre-trained FCN to perform model inference in a second or operationalphase. FIG. 5 illustrates a second phase for operational or simulationuse of the autonomous vehicle wheel detection system 210 in an exampleembodiment. As shown in FIG. 5, the autonomous vehicle wheel detectionsystem 210 can receive real-world operational image data, includingstatic images and image sequences, from the image data collection system205. The image data collection system 205 can include an array ofperception information gathering devices, sensors, and/or imagegenerating devices on or associated with an autonomous vehicle, similarto the perception information gathering devices of the image datacollection system 201, except that image data collection system 205collects real-world operational image data and not training image data.As described in more detail herein, the autonomous vehicle wheeldetection system 210 can process the input real-world operational imagedata by applying the one or more trained vehicle wheel object contourclassifiers 211 to produce vehicle wheel object data 220, which can beused by other autonomous vehicle subsystems to configure or control theoperation of the autonomous vehicle. As also described above, semanticsegmentation or similar processes can be used for the vehicle wheelobject extraction from the real-world image data.

To obtain the tradeoff between inference speed and model precision, anexample embodiment can resize all input images to a width of 512 and aheight of 288 so that we achieve real-time (50 HZ) performance whilemaintaining a high accuracy (recall ≥0.9). Examples of ground truthimage and the corresponding prediction results are illustrated in FIGS.6 through 10. It can be seen that the trained model of the exampleembodiment achieves excellent results in various conditions, such asdifferent vehicle types (e.g., cars, trucks, etc.), different distances(proximal and distal), and different illumination conditions (e.g.,sunny, shade, etc.).

FIG. 6 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 4, among other training images. FIG. 6 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result.

FIG. 7 (lower image half) illustrates another example ground truth labelmap that can be used for training the segmentation model according to anexample embodiment. FIG. 7 (upper image half) also illustrates theblended visualization of the original example image combined with theground truth.

FIG. 8 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 7, among other training images. FIG. 8 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result.

FIG. 9 (lower image half) illustrates yet another example ground truthlabel map that can be used for training the segmentation model accordingto an example embodiment. FIG. 9 (upper image half) also illustrates theblended visualization of the original example image combined with theground truth.

FIG. 10 (lower image half) illustrates an example predicted label mapusing the trained segmentation model trained with the example image ofFIG. 9, among other training images. FIG. 10 (upper image half) alsoillustrates the blended visualization of the original example imagecombined with the prediction result.

The autonomous vehicle wheel detection system 210 can process the inputimage data with the one or more trained classifiers 211 to producevehicle wheel object data 220, which can be used by other autonomousvehicle subsystems to configure or control the operation of theautonomous vehicle. Thus, a system and method for vehicle wheeldetection for autonomous vehicle control are disclosed.

Referring now to FIG. 11, a flow diagram illustrates an exampleembodiment of a system and method 1000 for vehicle wheel detection. Theexample embodiment can be configured to: receive training image datafrom a training image data collection system (processing block 1010);obtain ground truth data corresponding to the training image data(processing block 1020); perform a training phase to train one or moreclassifiers for processing images of the training image data to detectvehicle wheel objects in the images of the training image data(processing block 1030); receive operational image data from an imagedata collection system associated with an autonomous vehicle (processingblock 1040); and perform an operational phase including applying thetrained one or more classifiers to extract vehicle wheel objects fromthe operational image data and produce vehicle wheel object data(processing block 1050).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150 and/or the image processingmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of datacommunications. In many cases, the mobile device 130 is a handheld,portable device, such as a smart phone, mobile phone, cellulartelephone, tablet computer, laptop computer, display pager, radiofrequency (RF) device, infrared (IR) device, global positioning device(GPS), Personal Digital Assistants (PDA), handheld computers, wearablecomputer, portable game console, other mobile communication and/orcomputing device, or an integrated device combining one or more of thepreceding devices, and the like. Additionally, the mobile device 130 canbe a computing device, personal computer (PC), multiprocessor system,microprocessor-based or programmable consumer electronic device, networkPC, diagnostics equipment, a system operated by a vehicle 119manufacturer or service technician, and the like, and is not limited toportable devices. The mobile device 130 can receive and process data inany of a variety of data formats. The data format may include or beconfigured to operate with any programming format, protocol, or languageincluding, but not limited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150 and/or the image processingmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of inter-processor networked data communications. In many cases, the network resource122 is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150 and/or the image processing module200 to interact with one or more components of a vehicle subsystem.These client devices 132 or 122 may include virtually any computingdevice that is configured to send and receive information over anetwork, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150 and/or the image processing module 200can be implemented using systems that enhance the security of theexecution environment, thereby improving security and reducing thepossibility that the in-vehicle control system 150 and/or the imageprocessing module 200 and the related services could be compromised byviruses or malware. For example, the in-vehicle control system 150and/or the image processing module 200 can be implemented using aTrusted Execution Environment, which can ensure that sensitive data isstored, processed, and communicated in a secure way.

FIG. 12 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a data processor; a memoryfor storing a detection system, executable by the data processor; and animage data collection system associated with an autonomous vehicle, theimage data collection system being in data communication with the dataprocessor, the detection system being configured to: receive, by use ofthe data processor, image data from the image data collection system;extract, by use of the data processor, a vehicle wheel object of avehicle other than the autonomous vehicle from the image data using atleast one trained classifier, the at least one classifier being trainedwith ground truth data and training image data from a training imagedata collection system, operation of the at least one trained classifierbeing modified using ancillary data representing a context in which theautonomous vehicle is operating, the context including a direction oftravel of the autonomous vehicle, a speed of the autonomous vehicle, anda status of the autonomous vehicle; produce, by use of the dataprocessor, vehicle wheel object data related to a wheel of the vehiclefrom the extracted vehicle wheel object, the vehicle wheel object datacomprising vehicle wheel contour data corresponding to a contoursurrounding the wheel of the vehicle; and infer, by use of the dataprocessor, an intention of the vehicle from which the vehicle wheelobject is extracted based on the vehicle wheel object data and thecontext of the autonomous vehicle.
 2. The system of claim 1 wherein theground truth data is obtained from a manual image annotation or labelingprocess.
 3. The system of claim 1 being further configured to generate ablended visualization of a raw image combined with a ground truthcorresponding to the ground truth data.
 4. The system of claim 1 beingfurther configured to generate ground truth data, wherein the generatingthe ground truth data comprises filling in an interior region defined bythe contour.
 5. The system of claim 1 being further configured to use afully convolutional neural network (FCN) as a machine learning model totrain the at least one classifier.
 6. The system of claim 1 beingfurther configured to use a machine learning model with semanticsegmentation using dense upsampling convolution (DUC) and semanticsegmentation using hybrid dilated convolution (HDC), to train the atleast one classifier.
 7. The system of claim 1 being configured togenerate an object-level contour detection for the extracted vehiclewheel object of the image data.
 8. A method comprising: receiving, byuse of a data processor and an image data collection system associatedwith an autonomous vehicle, image data from the image data collectionsystem, the image data collection system being in data communicationwith the data processor; extracting, by use of the data processor, avehicle wheel object of a vehicle other than the autonomous vehicle fromthe image data using at least one trained classifier, the at least oneclassifier being trained with ground truth data and training image datafrom a training image data collection system, operation of the at leastone trained classifier being modified using ancillary data representinga context in which the autonomous vehicle is operating, the contextincluding a direction of travel of the autonomous vehicle, a speed ofthe autonomous vehicle, and a status of the autonomous vehicle;producing, by use of the data processor, vehicle wheel object datarelated to a wheel of the vehicle from the extracted vehicle wheelobject, the vehicle wheel object data comprising vehicle wheel contourdata corresponding to a contour surrounding the wheel of the vehicle;and inferring, by use of the data processor, an intention of the vehiclefrom which the vehicle wheel object is extracted based on the vehiclewheel object data and the context of the autonomous vehicle.
 9. Themethod of claim 8 wherein a number of the vehicle is at least two,wherein a number of the vehicle object extracted from the image data isat least two, wherein a number of the wheel in the vehicle wheel objectdata is at least two, wherein a number of the contour is at least two.10. The method of claim 8 comprising performing a training phase totrain the at least one classifier for processing at least one image ofthe training image data to detect the vehicle wheel object in the atleast one image.
 11. The method of claim 10 wherein the training phasecomprises a first phase and a second phase, wherein the at least oneclassifier is trained with the ground truth data and the training imagedata in the first phase, wherein the at least one classifier is trainedwith real-time image data in the second phase.
 12. The method of claim11 wherein a process of training the at least one classifier in thefirst phase is offline.
 13. The method of claim 8 comprising obtainingthe ground truth data, wherein the ground truth data is generated fromextracting object features from the training image data.
 14. The methodof claim 13 wherein the extracting object features from the trainingimage data comprises obtaining at least one of: information of positionsof objects and boundaries of the objects, in each frame of the trainingimage data.
 15. A non-transitory machine-useable storage mediumembodying instructions which, when executed by a machine, cause themachine to: receive image data from an image data collection systemassociated with an autonomous vehicle; extract a vehicle wheel object ofa vehicle other than the autonomous vehicle from the image data using atleast one trained classifier, the at least one classifier being trainedwith ground truth data and training image data from a training imagedata collection system, operation of the at least one trained classifierbeing modified using ancillary data representing a context in which theautonomous vehicle is operating, the context including a direction oftravel of the autonomous vehicle, a speed of the autonomous vehicle, anda status of the autonomous vehicle; produce vehicle wheel object datarelated to a wheel of the vehicle from the extracted vehicle wheelobject, the vehicle wheel object data comprising vehicle wheel contourdata corresponding to a contour surrounding the wheel of the vehicle;and infer an intention of the vehicle from which the vehicle wheelobject is extracted based on the vehicle wheel object data and thecontext of the autonomous vehicle.
 16. The non-transitorymachine-useable storage medium of claim 15 wherein the ground truth datais generated from: extracting object features from the training imagedata, and providing a pixel-level object label or a bounding box foreach object identified in the training image data.
 17. Thenon-transitory machine-useable storage medium of claim 16, wherein thebounding box is a rectangular box of a size corresponding to the contoursurrounding the wheel of the vehicle.
 18. The non-transitorymachine-useable storage medium of claim 15 wherein each of the imagedata collection system and the training image data collection systemcomprises an array of sensors.
 19. The non-transitory machine-useablestorage medium of claim 18 wherein the sensors comprise at least one ofa camera, a laser device, a light detection and ranging (LIDAR) device,a global positioning system (GPS) device, a sound navigation and ranging(sonar) device, and a radio detection and ranging (radar) device. 20.The non-transitory machine-useable storage medium of claim 15 whereinthe at least one classifier is trained by training a machine learningmodel using stochastic gradient descent (SGD) for data iterations.